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Business cycle analysis and VARMA models

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Author Info
Kascha, Christian
Mertens, Karel

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Abstract

Can long-run identified structural vector autoregressions (SVARs) discriminate between competing models in practice? Several authors have suggested SVARs fail partly because they are finite-order approximations to infinite-order processes. We estimate vector autoregressive moving average (VARMA) and state space models, which are not misspecified, using simulated data and compare true with estimated impulse responses of hours worked to a technology shock. We find few gains from using VARMA models. However, state space algorithms can outperform SVARs. In particular, the CCA subspace method consistently yields lower mean squared errors, although even these estimates remain too imprecise for reliable inference. The qualitative differences for algorithms based on different representations are small. The comparison with estimation methods without specification error suggests that the main problem is not one of working with a VAR approximation. The properties of the processes used in the literature make identification via long-run restrictions difficult for any method.

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Publisher Info
Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 33 (2009)
Issue (Month): 2 (February)
Pages: 267-282
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Handle: RePEc:eee:dyncon:v:33:y:2009:i:2:p:267-282

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Related research
Keywords: Structural VARs VARMA State space models Business cycles;

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References listed on IDEAS
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  1. Edward C. Prescott, 1986. "Theory ahead of business cycle measurement," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall, pages 9-22. [Downloadable!]
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  2. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-73, September. [Downloadable!] (restricted)
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  3. George Kapetanios, 2002. "A Note on an Iterative Least Squares Estimation Method for ARMA and VARMA Models," Working Papers 467, Queen Mary, University of London, Department of Economics. [Downloadable!]
  4. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2004. "A Critique of Structural VARs Using Real Business Cycle Theory," Levine's Bibliography 122247000000000518, UCLA Department of Economics. [Downloadable!]
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  5. Kapetanios, George, 2003. "A note on an iterative least-squares estimation method for ARMA and VARMA models," Economics Letters, Elsevier, vol. 79(3), pages 305-312, June. [Downloadable!] (restricted)
  6. Ellen McGrattan, 2006. "Measurement with Minimal Theory," 2006 Meeting Papers 338, Society for Economic Dynamics.
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  7. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2006. "Assessing Structural VARs," NBER Working Papers 12353, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  8. Christopher J. Erceg & Luca Guerrieri & Christopher Gust, 2005. "Can Long-Run Restrictions Identify Technology Shocks?," Journal of the European Economic Association, MIT Press, vol. 3(6), pages 1237-1278, December. [Downloadable!] (restricted)
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  9. Bauer, Dietmar, 2005. "Estimating Linear Dynamical Systems Using Subspace Methods," Econometric Theory, Cambridge University Press, vol. 21(01), pages 181-211, February. [Downloadable!]
  10. Lawrence J. Christiano & Martin Eichenbaum, 1991. "Identification and the Liquidity Effect of a Monetary Policy Shock," NBER Working Papers 3920, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  11. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March. [Downloadable!] (restricted)
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  12. Cooley, Thomas F. & Dwyer, Mark, 1998. "Business cycle analysis without much theory A look at structural VARs," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 57-88. [Downloadable!] (restricted)
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